Semantic Web - Volume 1, issue 1,2

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ISSN 1570-0844 (P)
ISSN 2210-4968 (E)

Impact Factor 2018: 2.224

The journal Semantic Web – Interoperability, Usability, Applicability is an international and interdisciplinary journal bringing together researchers from various fields which share the vision and need for more effective and meaningful ways to share information across agents and services on the future Internet and elsewhere.

As such, Semantic Web technologies shall support the seamless integration of data, on-the-fly composition and interoperation of Web services, as well as more intuitive search engines. The semantics – or meaning – of information, however, cannot be defined without a context, which makes personalization, trust and provenance core topics for Semantic Web research.

New retrieval paradigms, user interfaces and visualization techniques have to unleash the power of the Semantic Web and at the same time hide its complexity from the user. Based on this vision, the journal welcomes contributions ranging from theoretical and foundational research over methods and tools to descriptions of concrete ontologies and applications in all areas. Papers which add a social, spatial and temporal dimension to Semantic Web research, as well as application-oriented papers making use of formal semantics, are especially welcome.

Abstract: Perhaps the most fundamental notion underlying the desiderata for a successful Semantic Web is Semantic Interoperability. In this context, ontologies have been more and more recognized as one of the enabling technologies. This paper defends the view that an approach which neglects the role of ontologies as reference conceptual models cannot meet the requirements for full semantic interoperability. The paper starts by offering an engineering view on ontology engineering, discussing the relation between ontologies as conceptual models and ontologies as codification artifacts. Furthermore, it discusses the importance of foundational theories and principles to the design of ontology (conceptual) modeling languages…and models, emphasizing the fundamental role played by true ontological notions in this process. Finally, it elaborates on the need for proper tools to handle the complexity of ontology engineering in industrial scenarios and complex domains. These tools include ontological design patterns as well as well-founded computational environments to support ontology creation, verification and validation (via model simulation).
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Abstract: The Semantic Web emphasizes encoding over modeling. It is built on the premise that ontology engineers can say something useful about the semantics of vocabularies by expressing themselves in an encoding language for automated reasoning. This assumption has never been systematically tested and the shortage of documented successful applications of Semantic Web ontologies suggests it is wrong. Rather than blaming OWL and its expressiveness (in whatever flavor) for this state of affairs, we should improve the modeling techniques with which OWL code is produced. I propose, therefore, to separate the concern of modeling from that of encoding, as it is…customary for database or user interface design. Modeling semantics is a design task, encoding it is an implementation. Ontology research, for applications in the Semantic Web or elsewhere, should produce languages for both. Ontology modeling languages primarily support ontological distinctions and secondarily (where possible and necessary) translation to encoding languages.
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Abstract: The early phases of the software-development lifecycle (SDLC) for enterprise-scale systems – in particular, requirements elicitation, functional design, and technical design – are difficult to automate because they involve the application of several different kinds of domain knowledge. In this paper, we will provide a vision of how creating semantic models of domain knowledge used in each phase, and defining semantic representation, through which tools in the various phases can communicate knowledge across phases, can help provide more automation both within and across these phases. We refer to the collection of semantic models needed to support this automation as the…semantic bus for software development. We refer to the semi-automated process that we envision making use of this bus to support the SDLC as, Model-Assisted Software Development (MASD), which is a variation on the Model-Driven Development idea. We will describe tooling we have built which realizes part of this vision, and will outline a roadmap of potential research opportunities in this space.
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Abstract: Space and time have not received much attention on the Semantic Web so far. While their importance has been recognized recently, existing work reduces them to simple latitude-longitude pairs and time stamps. In contrast, we argue that space and time are fundamental ordering relations for knowledge organization, representation, and reasoning. While most research on Semantic Web reasoning has focused on thematic aspects, this paper argues for a unified view combining a spatial, temporal, and thematic component. Besides their impact on the representation of and reasoning about individuals and classes, we outline the role of space and time for ontology modularization,…evolution, and the handling of vague and contradictory knowledge. Instead of proposing yet another specific methodology, the presented work illustrates the relevance of space and time using various examples from the geo-sciences.
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Abstract: A major source of interoperability problems on the Semantic Web are the different vocabularies used in metadata descriptions. This paper argues that instead of solving interoperability problems we should focus more effort on avoiding the problems in the first place, in the spirit of Albert Einstein's quote “Intellectuals solve problems, geniuses prevent them”. For this purpose, coordinated collaborative development of open source vocabularies and centralized publication of them as public vocabulary services are proposed. Methods, guidelines, and tools to facilitate this have been developed on a national level in the Finnish FinnONTO initiative, and are now in pilot use with…applications and promising first results
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Abstract: The realization of Semantic Web reasoning is central to substantiating the Semantic Web vision. However, current mainstream research on this topic faces serious challenges, which forces us to question established lines of research and to rethink the underlying approaches. We argue that reasoning for the Semantic Web should be understood as “shared inference,” which is not necessarily based on deductive methods. Model-theoretic semantics (and sound and complete reasoning based on it) functions as a gold standard, but applications dealing with large-scale and noisy data usually cannot afford the required runtimes. Approximate methods, including deductive ones, but also approaches based on…entirely different methods like machine learning or nature-inspired computing need to be investigated, while quality assurance needs to be done in terms of precision and recall values (as in information retrieval) and not necessarily in terms of soundness and completeness of the underlying algorithms.
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Abstract: The Semantic Web is about to grow up. By efforts such as the Linking Open Data initiative, we finally find ourselves at the edge of a Web of Data becoming reality. Standards such as OWL 2, RIF and SPARQL 1.1 shall allow us to reason with and ask complex structured queries on this data, but still they do not play together smoothly and robustly enough to cope with huge amounts of noisy Web data. In this paper, we discuss open challenges relating to querying and reasoning with Web data and raise the question: can the burgeoning Web of Data ever…catch up with the now ubiquitous HTML Web?
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Abstract: Nowadays, building ontologies is a time consuming task since they are mainly manually built. This makes hard the full realization of the Semantic Web view. In order to overcome this issue, machine learning techniques, and specifically inductive learning methods, could be fruitfully exploited for learning models from existing Web data. In this paper we survey methods for (semi-)automatically building and enriching ontologies from existing sources of information such as Linked Data, tagged data, social networks, ontologies. In this way, a large amount of ontologies could be quickly available and possibly only refined by the knowledge engineers. Furthermore, inductive incremental learning…techniques could be adopted to perform reasoning at large scale, for which the deductive approach has showed its limitations. Indeed, incremental methods allow to learn models from samples of data and then to refine/enrich the model when new (samples of) data are available. If on one hand this means to abandon sound and complete reasoning procedures for the advantage of uncertain conclusions, on the other hand this could allow to reason on the entire Web. Besides, the adoption of inductive learning methods could make also possible to dial with the intrinsic uncertainty characterizing the Web, that, for its nature, could have incomplete and/or contradictory information.
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Abstract: With the web of data, the semantic web can be an empirical science. Two problems have to be dealt with. The knowledge soup problem is about semantic heterogeneity, and can be considered a difficult technical issue, which needs appropriate transformation and inferential pipelines that can help making sense of the different knowledge contexts. The knowledge boundary problem is at the core of empirical investigation over the semantic web: what are the meaningful units that constitute the research objects for the semantic web? This question touches many aspects of semantic web studies: data, schemata, representation and reasoning, interaction, linguistic grounding, etc.